Businesses face many difficulties in determining and updating safety stock levels for their purchased and manufactured items, including both products and components. For example, it is not uncommon for a production business to have tens of thousands, and often many more, of such items. However, each item's safety stock level may have an adverse effect on two opposing business goals: service level (which can suffer from product or component shortages); and return on inventory investment (which can suffer from product or component excesses). The balance that achieves these two goals is optimal safety stock—just enough “extra” of each product and component to achieve the desired service level, but no more.
Many methods of determining safety-stock levels exist to improve service level and inventory investment. For example, Material Requirements Planning (“MRP”) techniques and software are often used to determine materials required to support a forecast mix and volume of top-level products. Top-level products are items ordered by, and sent to, a customer external to the supply chain. A top-level product is generally not represented by a planning bill-of-materials. As used herein, MRP also includes the bill-of-materials (“BOM”) and supply-chain structures that enable MRP to determine component requirements for manufactured and purchased items.
As businesses reduce their supply-chain lead times, irregular demand variation (as contrasted with other types of variation, such as slope, seasonality, cycle and forecast-inaccuracy) comprises a larger consideration when determining safety stock. Irregular variation increases on a power curve as lead time decreases linearly. At a minimum, acceptable prior art methods of determining demand-irregularity safety-stock levels utilize four inputs: 1) mean usage or demand; 2) irregular-demand variation around the mean; 3) target service level; and 4) lead or replenishment time. While this list of inputs is short, the effort to gather and analyze these inputs must be multiplied by the tens of thousands or more components a business may have.
Additionally, mean and variation analyses may require time-series data that have been cleaned of invalid data points, that have enough data points to be reliable, and that have been normalized for slope and seasonality. All of this may take place for the time-series of each of the tens of thousands of components. Further, optimal service level for a component must correctly represent an aggregate of the target service levels of the products which consume the component. However, a component's target service level must often be assigned arbitrarily and on the side of caution, which generally results in excess safety stock.
Due to complexities in the prior art methods of determining safety stock levels, many businesses are unable to invest the time required to obtain the inputs necessary to determine safety-stock level. As a result, they are also unable to achieve and sustain either of the two fundamental but opposing business goals of having a suitable service level and a suitable return on inventory investment.